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Estimating the return to schooling using the Mincer equation

The Mincer equation gives comparable estimates
of the average monetary returns of one additional year of education

Elevator pitch

The Mincer equation—arguably the most widely
used in empirical work—can be used to explain a host of economic, and even
non-economic, phenomena. One such application involves explaining (and
estimating) employment earnings as a function of schooling and labor market
experience. The Mincer equation provides estimates of the average monetary
returns of one additional year of education. This information is important
for policymakers who must decide on education spending, prioritization of
schooling levels, and education financing programs such as student
loans.

Key findings

Pros

Earnings can be explained as a
function of schooling and labor market experience using the
Mincer equation; this provides policymakers with important
information about how to invest in education.

Due to the comparability of
Mincerian results, individuals can make use of these results to
help guide their personal decisions about how much schooling
they should invest in.

Recent studies using the Mincer
equation indicate that tertiary education, as opposed to primary
education, may now provide the greatest returns to schooling;
this represents a shift in the conventional wisdom.

Cons

The relationship between schooling
and earnings does not necessarily imply causality.

Earnings functions provide private
(i.e. individual) returns to schooling, whereas
government/public costs and other benefits are needed to
estimate social rates of return.

As economies become more complex and
technological developments alter the demand for education,
decades-old cross-sectional data may not be informative about
returns to current investment decisions.

Author's main message

The Mincer equation suggests that each
additional year of education produces a private (i.e. individual) rate of
return to schooling of about 5–8% per year, ranging from a low of 1% to more
than 20% in some countries. Globally, the returns to tertiary education are
highest, followed by primary and then secondary schooling; this represents a
significant reversal from many studies’ prior results. Policymakers can
learn much from Mincerian results; for instance, further expansion of
university education appears to be very worthwhile for the individual,
meaning that governments need to find ways to make financing more readily
available, and that high rates of return are found through investment in
girls’ education.

Motivation

Private rates of return to schooling are
undisputable. They are frequently used to explain individual behavior with
respect to educational choices and to indicate productivity [2]. They can be used to analyze the
distributional effects of education finance programs, which can guide public
policy with respect to the design of programs and the crafting of incentives
that promote both private and public educational investment. A good example
is student finance, where returns to schooling estimates can be used to
design student loan programs. They can help policymakers establish repayment
rates, especially if income-contingent programs are adopted. This evidence
can also be used to assess the prospective benefit of enrolling in
post-secondary education, as the decision to enroll depends on the long-term
outcomes of degree completion, earnings differences, degree completion, and
field of study. As such, understanding how to assess returns to schooling
and being able to appropriately apply those results to policy decisions is a
critical skill for policymakers to have at their disposal.

Economists use so-called earnings functions to
estimate returns to education, with the Mincer equation being arguably the
most widely used in empirical work. It provides the estimations necessary to
evaluate returns to schooling from a monetary perspective, and allows for
the direct comparison of results. Empirical evidence on returns to
investment in education provides useful indicators—typically in the form of
projected future wages—that help individuals decide how to invest in their
own human capital. These indicators can also be used as a basis for setting
public policy with respect to investment in education.

Mincerian earnings functions have been estimated
for a large number of countries and across many different demographic
groups. The Mincer equation has become ubiquitous, to the point where people
no longer reference the original source or may not even use the term
“Mincerian” [3]. Its simple structure and
parsimonious nature make the Mincer equation the most appropriate way to
study earnings. On top of this, it is also a flexible model, which allows
researchers to include other variables, while at the same time it delivers a
precise method of modeling the relationship between earnings, schooling, and
experience.

Discussion of pros and cons

The concept of schooling as a personal
investment goes back to at least Adam Smith. Following the human capital
revolution in economic thought in the 1960s, the worker’s educational level
was analyzed extensively, with the microeconomics focus largely being on the
returns to education. That is, the measure of the future economic benefits
to an individual or society from increasing the amount of education
undertaken. There are essentially two classes of estimation methods: one
that uses the internal-rate-of-return procedure, and another that
approximates this procedure by fitting an earnings function to individual
data sets. The internal rate of return is found by setting the discounted
value of costs and benefits over time equal to zero and finding the implicit
discount rate, but this is not examined further in the present article as it
is beyond the scope of the Mincer equation or other standard earnings
functions.

The Mincer equation explains earnings as a
function of schooling and labor market experience, giving a clear sense of
the average monetary returns of one additional year of schooling. The rate
of return to investment in schooling is presented in a simple and comparable
format that permits estimates of the profitability of schooling, thus
allowing people to use this information for investment decision
purposes.

The interest in ascertaining the value of the
investment in education has increased over time. Worldwide, education
spending has increased from 3.6% of GDP in 1970 to more than 4.8% of GDP in
2011. At the same time, individuals and their families invest a significant
amount out of their own pockets for tuition and other education-related
expenses. The student’s foregone earnings represent the largest costs
incurred by individuals and their families while studying. Is this massive
investment in schooling justified? One way to analyze this question is to
estimate the costs and benefits of the investment, for which the Mincer
equation is ideally suited.

Although most rate of return studies attempt to
estimate the returns to schooling for a country, region, or level of
schooling, lately there has been interest in more disaggregated information
such as returns for certain population groups categorized according to
specific characteristic, e.g. ethnic, linguistic, religious groups, persons
with disabilities, and so on. Estimating Mincer equations for different
groups such as males and females or ethnic groups can be used to study the
extent of labor market discrimination. Returns to schooling for women are
used to justify further investments in girls’ schooling. Returns to
schooling in developing countries are used to justify international goals
for getting all children into school. In developed countries, the massive
investments in higher education, rising tuition levels, and increasing
student debt loads are calling into question the overall attractiveness of
educational investment.

It should be noted that the relationship between
schooling and earnings does not necessarily imply causality. Moreover, since
earnings functions provide private returns to schooling, the only cost taken
into account is the foregone earnings associated with attending school
instead of earning a working wage. To obtain an estimate of the social
return to schooling, one needs to identify other costs (e.g.
government/public costs of education) and benefits outside of wages, such as
health benefits. Moreover, despite the usefulness of rate of return
estimates, rapidly changing conditions (i.e. technological developments and
increasing overall economic complexity) degrade the value of using
historical cross-sectional data to make current investment decisions.
Another issue is that the simple Mincer equation estimates an average
marginal rate of return; however, the rate of return can vary with the
number of years of schooling, and can decrease over the working lifecycle
and by cohort; as seen in the Illustration, the
returns to another year of schooling change—typically decline—as the average
level of schooling rises over time.

Interpretation of results from an
earnings function

The form of the earnings function—that is,
which variables to include and how they enter the equation—is important
[4]. Researchers typically use
hourly earnings as the dependent variable (the outcome of interest) in
these calculations. However, the time frame over which earnings are
measured is often determined by necessity: some data sets report annual
earnings whereas others report weekly or hourly wages. Because
individuals with higher levels of schooling tend to work more, the
resulting return to schooling will be greater when using weekly or
annual earnings than hourly earnings [5]. This must be kept in mind
when constructing and interpreting the results from earnings
functions.

Next to education, work experience is
another important (explanatory) variable that accounts for the post
schooling accumulation of human capital and therefore consequently
affects earnings. Age is commonly used to approximate experience, though
one must account for each individual’s number of years spent in school
when doing so as this has an impact on the individual’s starting
qualifications. In developing countries where children may not start
school on time due to, for example, illness, cost, or distance, or when
there is a high incidence of serial repeaters, there may be a bit of
measurement error in using years of schooling to approximate experience.
However, omitting experience would lead to biased results due to the
relationship between experience and other relevant characteristics. In
particular, experience and schooling are negatively correlated, i.e.
when examining people of the same age, those with more years of
schooling have less work experience. And both schooling and experience
are positively correlated with earnings. This means that omitting
experience could lead to an underestimation of the effects of schooling
on earnings [6].

Moreover, one of the limitations of the
Mincer equation is the assumption that returns to experience are the
same at all levels of education. The problem of heterogeneous experience
premiums is noted in the literature, but no agreed solution has so far
appeared. Furthermore, experience not only captures on-the-job learning
and the deterioration of skills, but also picks up institutional or
contractual issues, such as when firms offer earning profiles that are
skewed toward older workers in order to prevent shirking. In labor
economics one speaks of the efficiency wage hypothesis, which argues
that some managers may have an incentive to pay employees a salary above
market rates in order to reduce the costs associated with turnover,
especially in industrial sectors where the costs of replacing labor are
high. Part of this incentive wage may be captured by the experience
coefficient.

The interpretation issues that arise when
reading the results of an earnings function are illustrated in the
compilation of returns to schooling from 139 economies (Figure 1). The study shown is typical, in
the sense that it uses representative, structured surveys and estimates
earnings profiles using cross-section data. The first column shows the
results for the typical Mincerian function with years of schooling as a
continuous variable. The results represent workers with positive
earnings in the labor market, which is a large sample. Overall, the
returns to schooling are highest in sub-Saharan Africa, at 12.5%.
Returns are also high in high-income economies, Latin America and the
Caribbean, and East Asia and the Pacific, at 10%, 9.3%, and 9%,
respectively. The lowest returns are found in the Middle East and North
Africa, at only 6.5%. Overall, the global average return is just under
10% [7].

Figure 1 also shows returns by
gender. On average, returns to schooling are significantly higher for
women than for men. The lowest returns to schooling are for men in the
Middle East and North Africa, at only 6%. The highest returns to
schooling are for women in sub-Saharan Africa, at almost 15%.

Global estimates of the returns to
schooling

Most studies that present compilations of
returns to schooling suffer from problems of comparability due to data
sample coverage and methodology. One common problem is that surveys may
not accurately reflect population averages. Researchers also typically
include many different independent (explanatory) variables in their
models, which may affect estimates of the returns to schooling, and
different studies rarely use the same models. The typical Mincerian
earnings function uses a limited number of variables—earnings,
schooling, and experience. But, some researchers add many other
variables including sector of employment, region, and so on. They may
also specify the variables differently, all of which makes comparability
difficult. However, recent estimates provide results for a large number
of countries, with one new compilation presenting comparable estimates
of the private returns to schooling using 911 household surveys from 139
economies [7]. The study maintains a
constant definition for the outcome variable, the set of control
(independent explanatory) variables, the sample definition with respect
to age, and the estimation method for all surveys represented in the
sample.

The global average private rate of
return to a year of schooling is about 10%. New evidence
suggests that the return to another year of schooling (marginal
return) is indeed positive, significant, and more concentrated
around the mean than previously thought, at 9.7% overall [7].

The returns to schooling are higher
in high-income economies and in sub-Saharan Africa.

The returns to schooling are higher
for women than for men. Women receive a rate of return of 11.5%,
compared to 9.6% for men [7]. (However, simply
applying the Mincer earnings function for women may be biased,
for instance, due to incorrect measurement of experience,
selection due to the fact that women are less likely to be wage
earners, and because women often have discontinuous labor force
participation.)

Though the returns to schooling
decline as the supply of schooling in a country increases, they
tend to decline only modestly over time, by no more than two
percentage points in a given decade, and actually much less than
that in recent years. Using global data, returns to schooling
are shown to have declined significantly over time, but at a
much slower rate than has occurred with the expansion in
schooling, especially since the late 1990s [7]. While the supply of
schooling has expanded by almost 50% worldwide since 1980, the
returns to schooling have declined by only 3.5 percentage
points, or 0.1% per year. At the same time, the average length
of schooling has increased by more than 3 years, or 2% per year
worldwide. On average, another year of schooling leads to a
reduction of the returns to schooling by one percentage
point.

Determining the most valuable return
to educational investment

Individuals invest in a level of education
or a degree program, rather than in years of education. The Mincerian
equation is a flexible specification. Years of schooling can be replaced
with other variables to represent the levels of schooling (e.g. specific
types of degree programs). These become “dummy variables” (variables
with two values: 0 and 1) for each level of schooling. Since levels of
schooling are sequential—that is, you must complete primary to enter
secondary, and secondary to enter tertiary—the returns to schooling for
each level also incorporate the benefit of previous levels of
schooling.

One of the long-standing conclusions from
the literature is that returns to schooling are highest at the primary
education level, but this is no longer considered true [7]. Globally, the returns to
tertiary education are highest, followed by primary; the lowest returns
are for secondary schooling (see Figure 2). On average, the
returns to tertiary education are 17%, while they are 10% for primary
education. The highest returns to tertiary are in Africa, at 22%, where
one also finds the highest returns to primary schooling, at 13%. To some
extent, the effect on primary education may be driven by an increase in
compulsory education. That is, as countries develop and education
systems expand, primary school becomes compulsory and universal, making
it difficult or impossible to estimate returns to education, or to
compare them to older workers who may have lower wages for other
reasons.

Do skills gained from schooling or the
acquisition of credentials lead to increased wages?

The earnings premium associated with level
of education suggests that productivity increases as people acquire
additional qualifications. An alternative view is that earnings increase
with education due to credential effects. This refers to the idea that
higher levels of schooling are associated with higher earnings, not
because they directly raise productivity, but because they certify that
the worker is likely to be productive. In this sense, education merely
sorts workers according to their unobserved attributes; it does not
necessarily augment their intrinsic productivity. For public policy
reasons it is important to distinguish between the human capital
(productivity) and screening hypotheses about returns to education. In
very basic terms, these two hypotheses mean, respectively: schooling
imparts skills that enhance productivity; hence, increases in earnings
are due to the increased productivity brought about by investments in
schooling (human capital); while the screening hypothesis maintains that
employers select workers with higher qualifications to reduce their risk
of hiring someone with a lower capacity to learn; in this case, higher
earnings may not be due to productivity alone (screening). With these
concepts in mind, if the only purpose of schooling is to sort
prospective employees, then questions arise as to the appropriateness of
public investment in the expansion or improvement of schooling.

The above debate is typically presented as
an either/or argument. However, the observed correlation between
education and earnings shows that, at an individual level, schooling
does augment earnings. Some level of screening will likely exist, at
least at the entry level, and perhaps also influence the decision to
enroll. The earnings function can be used to examine the case for or
against screening.

Most tests of the screening hypothesis use
an earnings function. One test for screening examines returns for
individuals with a completed education versus those who dropped out; if
education serves as a signal (i.e. screening mechanism), then
certification from a course should convey more information to
prospective employers about an applicant’s ability than the number of
years of schooling. Returns for completers would thus be higher than
returns for non-completers. To examine the “weak versus the strong
version of the screening hypothesis” one draws a distinction between
employers paying irrational wages (i.e. wages that do not match the
applicant’s qualifications or experience) at the initial hiring point
(weak) versus those that continue paying such wages thereafter (strong).
Most applications of the “weak versus the strong version of the
screening hypothesis” do not find strong evidence of screening. A recent
analysis that uses rigorous evaluation techniques to compare the
earnings of workers who barely passed versus those who barely failed
high school exit exams finds little evidence of diploma screening
effects [8].

More rigorous tests of the screening
hypothesis involve taking advantage of “natural experiments” such as
changes in the school leaving age or college openings. By and large,
while some evidence of weak screening is revealed, education is
generally associated with higher earnings due to productivity rather
than to screening. Thus, investment in schooling continues to be a
worthwhile activity for individuals and societies to undertake [9].

The debate is ongoing about the external
validity and causality of rate of return estimates. More conclusive
evidence will emerge once data on the lifetime earnings profiles of
beneficiaries of voucher programs can be obtained, as many of these
programs use lotteries to assign places, thus giving researchers access
to randomized data. In 1981, Chile introduced nationwide school choice
by providing vouchers to any student wishing to attend a “voucher
school” (essentially, a private school participating in the program,
whereby the funding from the voucher would be used to pay the fees and
tuition). Beneficiaries of the vouchers obtained more schooling and
subsequently earned more than non-voucher students. Also, it is
estimated that formal sector earnings are higher for lottery winners in
Colombia’s large-scale government program which used a lottery to
distribute scholarships for private secondary school to socially
disadvantaged students.

Institutional factors have also been used to
more precisely estimate the returns to schooling, including the effect
of birth date. Those who are forced to remain in school because of their
birth date and the school attendance law receive the same rate of return
to education as those who voluntarily continue schooling.

Limitations and gaps

One of the perennial debates in educational
economics is the extent to which returns to schooling are correlated with
other factors, such as an individual’s ability, or if they are simply
exhibiting the effects of selection. It could be that generally more able
individuals choose to enroll in a study program or to undertake further
years of schooling because they are more academically capable and will
therefore benefit more from studying and subsequently be more likely to
obtain a higher paying job. In such a case, individual self-selection is
occurring, which may create an upward bias in the estimated rate of return
to schooling. Also, educational institutions may offer enrollment to more
able and easier to teach students, who are more likely to excel in the
respective programs. Again, this would tend to introduce an upward bias in
the returns to schooling.

Estimates of the returns to education based on
advanced econometric techniques that control for different characteristics
come to an average rate of return that is similar to the global average
presented in most reviews. These more selective studies focus on the
causality debate between schooling and earnings; they confirm that the
effect of ability and related factors does not have a significant impact on
the general results for returns to education. Still, more research on
causality is needed.

More recent analyses, which exploit data that
permit the disaggregation of earnings by years of completed schooling, have
questioned the linear nature of the earnings function approach. Furthermore,
due to ongoing and rapid technological progress, cross-sectional data based
on observations from many years in the past may produce biased estimates of
the returns to schooling. Some have even questioned whether it is still
possible to interpret the coefficient on schooling as a rate of return [10]. For instance, some researchers
argue that the literature following Mincer’s estimated returns to schooling
using cross-sectional data assumes that younger workers base their earnings
expectations on the current experiences of older workers. However, if prices
(i.e. cost of schooling, for example, tuition associated with university)
change over time and workers are able to at least partially anticipate these
changes, then estimates of the return to different schooling levels based on
cross-sectional data may not represent the ex-ante rates of return governing
human capital investment decisions. Relying on past cohorts to assess
current investment decisions requires several strong assumptions, such as
stability of the economic environment and perfect certainty of future
earnings streams. This is difficult in the current context, given that
emerging evidence suggests that wage patterns have changed substantially
over time, making it difficult to use cross sections to approximate
lifecycle earnings. One solution could be to follow actual cohorts over
their entire educational and employment lifecycles to measure their earnings
patterns in order to estimate the returns to education.

Finally, private rates of return are globally
applicable and useful. But, for many policy decisions, one needs to assess
the broader social rates of return, which include government/public spending
on the cost side and, among other things, monetary estimates of the benefits
associated with an individual’s investment in education and their children’s
education and health (e.g. declines in fertility, spouse’s health, job
search efficiency, social cohesion, reductions in crime, and so on). These
are not presented in this paper, since they require more data than is
currently available for the vast majority of countries, and because they
require estimation techniques that go beyond the capabilities of standard
human capital earnings functions. The Mincer equation can incorporate social
costs; more difficult is the inclusion of social benefits. Yet another
difficulty is the absence of data for the many social benefits.

Summary and policy advice

Education remains a positive, significant, and
profitable investment for individuals. On average, another year of education
produces a private rate of return to schooling in excess of 5−8% a year. As
such, there are few better investments one can make.

In a significant reversal, which is now seen
worldwide, returns to schooling are highest at the tertiary level. This has
important implications, as it will lead to an increase in demand for
tertiary education and put pressure on policymakers to expand university
education. This should not, however, come at the detriment of basic
education, since primary education is a fundamental human service, and
access to primary (and secondary) education is a prerequisite for entry into
university.

While women may receive less pay than men—though
the gender gap appears to be declining—an investment in education at all
levels provides a greater payoff for women than for men. The policy
implication from this is obvious. Countries need to continue efforts to
invest in girls’ education, and must ensure that females complete their
primary and secondary schooling. Because the payoff to university education
is so high, efforts to get women into university represent a sound
investment option, leading to overall efficiency gains and putting a further
dent into the gender gap.

Returns to schooling have declined only modestly
over a long period of time, despite massive worldwide investment in
schooling, meaning that education continues to be a worthy investment.
Policymakers would thus be wise to invest in basic education, continue to
improve the quality of schooling, and to pursue sensible policies for the
expansion of post-secondary education opportunities. For example, the high
returns to higher education will entice individuals to demand this level of
schooling. At the same time, high private returns suggest opportunities for
cost-sharing and innovative financing models. Thus, governments can impose
tuition and support those with financial constraints through student loans.
Even better would be to use future resources—the graduates’ future
earnings—to finance current education expansion. This would amount to income
contingent loan programs that base future payments on the earnings of
graduates. These types of loans mean that those who earn more will pay back
their loan more quickly. Those experiencing difficulties finding high-paying
jobs are allowed to repay their loans in smaller amounts, over a longer
period of time, or even have their debts forgiven altogether.

The Mincer equation has clearly helped advance
the fields of labor and educational economics. It has improved the
understanding of the determinants of earnings, the rate of return to
schooling, the demand for education, the impacts of discrimination, and the
importance of labor market experience and on-the-job training. Moreover, the
simple Mincer equation is now being used in other fields such as sociology
and anthropology. The Mincer equation is thus a genuinely valuable tool for
a wide-ranging group of researchers and policymakers; it should be relied
upon extensively for determining educational return estimates and will
continue to contribute to many other policy relevant fields in the
future.

Acknowledgments

The author thanks two anonymous referees and the
IZA World of Labor editors for many helpful suggestions on earlier drafts.
Previous work of the author (together with George Psacharopoulos [1] and Claudio Montenegro [7]) contains a larger number of
background references for the material presented here and has been used
intensively in all major parts of this article.